Main Approaches
- Over-representation analysis (ORA)
- Gene Set Enrichment Analysis (GSEA) … Functional Class Scoring(FCS)
- Pathway Topology-based methods
Session 5 : Enrichment analysis of Differentially Expressed Genes
Advanced R-course 2025
2025-11-21
Categorization of genes
Regrouping genes together in meaningful sets based on:
- involvement in a pathway
- presence in a specific cellular location
- shared molecular function
- other categorizations …
Note
To check if any categories are over-represented, compare the proportion of genes in each category in your gene list to the proportion in the background set and calculate how likely it is to see your observed proportion by chance.
Categorized gene sets : Databases
Human curated:
- Gene Ontology (GO)
- Biological Pathways (Reactome, KEGG, WikiPathways)
Domains / Patterns:
- Protein functional domains (SMART, InterPro)
- Transcription factor regulated (AnimalTFDB, JASPAR)
Experimental:
- Co-expressed genes (GeneFriends, COXPRESdb)
- Interactions (STRING, BioGRID)
- Hits from other studies
Molecular Signatures Database (MSigDB) is a popular resource that compiles many gene sets from various sources.
GO as a Directed Acyclic Graph (DAG)
GO terms form a hierarchical structure.
Each GO term may have:
- Parent terms (more general)
- Child terms (more specific)
Relationships include:
- is_a: subclass
- part_of: component of a larger process
Hierarchy is not strictly a tree; terms can have multiple parents.
Implications of the GO DAG
- A gene annotated to a specific child term is also annotated to all its parent terms.
- GO enrichment results often show clusters of related terms.
- Understanding the hierarchy helps interpret redundancy.
Hypergeometric testing
| Symbol | Description |
|---|---|
| \(N\) | Total number of genes (universe) |
| \(M\) | Number of DE genes (your list) |
| \(k\) | Number of genes in a category(GO term) |
| \(n\) | Number of DE genes found in that category |
We test if \(n\) is greater than expected by chance.
The hypergeometric test calculates the probability of observing \(n\) DE genes in a pathway of size \(k\), given the total number of genes \(N\) and the number of DE genes \(M\).
The p-value is calculated as: \[ P(X = n) = \frac{\binom{k}{n} \, \binom{N-k}{M-n}}{\binom{N}{M}} \]
Example in R
# Define parameters
N <- 20000 # Total number of genes
k <- 150 # Genes in the GO term
M <- 2000 # Total number of DE genes
n <- 30 # DE genes in the GO term
# Calculate p-value using hypergeometric test
p_value <- phyper(q = n - 1, # q = x-1 for upper tail
m = k, # number of white balls in population
n = N - k, # number of black balls in population
k = M, # number of draws
lower.tail = FALSE)
print(p_value)[1] 0.0001681513This p-value indicates the probability of observing 30 or more DE genes in the GO term by chance. A low p-value (e.g., < 0.05) suggests that the GO term is significantly over-represented among the DE genes.
Note
Remember to adjust for multiple testing when evaluating p-values across many GO terms.
Example: ORA with clusterProfiler
# Load necessary libraries
library(clusterProfiler)
library(org.Mm.eg.db)
library(ggplot2)
library(dplyr)
library(DESeq2)
library(qs)
set.seed(420)
dds <- qread("dds_all_genotype.qs")
uni_genes <- results(dds) |> as.data.frame() |>
filter(!is.na(padj)) |>
rownames()
de_genes_up <- results(dds) |> as.data.frame() |>
filter(!is.na(padj),padj <= 0.05, log2FoldChange > 0)|>
rownames()
de_genes_dn <- results(dds) |> as.data.frame() |>
filter(!is.na(padj),padj <= 0.05, log2FoldChange < 0)|>
rownames()
ego_result_up <- enrichGO(gene = de_genes_up,
universe = uni_genes,
OrgDb = org.Mm.eg.db,
keyType = "SYMBOL",
ont = "BP",
pAdjustMethod = "BH",
pvalueCutoff = 0.05,
qvalueCutoff = 0.2,
readable = TRUE)
# View results
#DT::datatable(head(ego_result_up))
# Visualize results
barplot(ego_result_up, font.size=14) +
ggtitle("GO Enrichment Analysis - Upregulated Genes : Barplot")
Visualisation with enrichplot - Dotplot
dotplot(ego_result_up, showCategory=10, font.size=14) +
ggtitle("GO Enrichment Analysis - Upregulated Genes : Dotplot")
Visualisation with enrichplot - Enrichment Map
# Add similarity matrix to the termsim slot of enrichment result
ego_result_up <- enrichplot::pairwise_termsim(ego_result_up)
# Enrichmap clusters the 30 most significant (by padj) GO terms
# to visualize relationships between terms
emapplot(ego_result_up, showCategory = 30) +
ggtitle("GO Enrichment Analysis - Upregulated Genes : Enrichment Map")
Visualisation with enrichplot - Cnetplot
cnetplot(ego_result_up, showCategory = 20, foldChange = NULL) +
ggtitle("GO Enrichment Analysis - Upregulated Genes : Cnetplot")
Things to Remember
Directional Gene Lists
- Separate upregulated and downregulated genes
- Perform enrichment analysis on each list separately
- Compare enriched categories between up and downregulated genes
- Provides insights into distinct biological processes affected
One search :
- Higher power (more genes)
- Lower enrichment
- Mixed effects (pathways)
Two searches:
- Easier interpretation
- Less power
- Higher enrichment
- Consider biological context and research question when choosing approach
- Using a background list can make a huge difference
- Validate findings with additional experiments or literature review
- Combine with other analyses (e.g., network analysis) for deeper insights
